magnum-v4-123b-gguf

This repo contains GGUF quants of the model. If you need the original weights, please find them here.

This is a series of models designed to replicate the prose quality of the Claude 3 models, specifically Sonnet and Opus.

This model is fine-tuned on top of mistralai/Mistral-Large-Instruct-2407.

Prompting

A typical input would look like this:

<s>[INST] SYSTEM MESSAGE\nUSER MESSAGE[/INST] ASSISTANT MESSAGE</s>[INST] USER MESSAGE[/INST]

SillyTavern templates

Below are Instruct and Context templates for use within SillyTavern.

<details><summary>context template</summary>

default SillyTavern template works fine

</details><br> <details><summary>instruct template</summary>

default SillyTavern template works fine

</details><br>

Axolotl config

<details><summary>See axolotl config</summary>

base_model: mistralai/Mistral-Large-Instruct-2407
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: anthracite-org/c2_logs_16k_mistral-large_v1.2
    type: sharegpt
    conversation: mistral
  - path: anthracite-org/kalo-opus-instruct-22k-no-refusal
    type: sharegpt
    conversation: mistral
  - path: lodrick-the-lafted/kalo-opus-instruct-3k-filtered
    type: sharegpt
    conversation: mistral
  - path: anthracite-org/nopm_claude_writing_fixed
    type: sharegpt
    conversation: mistral
  - path: anthracite-org/kalo_opus_misc_240827
    type: sharegpt
    conversation: mistral
  - path: anthracite-org/kalo_misc_part2
    type: sharegpt
    conversation: mistral
#chat_template: chatml
shuffle_merged_datasets: true
#default_system_message: "You are an assistant that responds to the user."
dataset_prepared_path: ./data/magnum-123b-data
val_set_size: 0.0
output_dir: ./data/123b-fft-out

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

adapter:
lora_model_dir:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
lora_fan_in_fan_out:

wandb_project: 123b-magnum-fft
wandb_entity:
wandb_watch:
wandb_name: alter-attempt-04
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0000015

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: unsloth
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 40
evals_per_epoch:
eval_table_size:
eval_max_new_tokens:
saves_per_epoch: 2
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.01
fsdp:
fsdp_config:
special_tokens:

</details><br>

Credits

We'd like to thank Eric Hartford for sponsoring the compute for this train. We would also like to thank all members of Anthracite who made this finetune possible.

Datasets

• anthracite-org/c2_logs_16k_mistral-large_v1.2
• anthracite-org/kalo-opus-instruct-22k-no-refusal
• lodrick-the-lafted/kalo-opus-instruct-3k-filtered
• anthracite-org/nopm_claude_writing_fixed
• anthracite-org/kalo_opus_misc_240827
• anthracite-org/kalo_misc_part2

Training

We used 8x mi300x GPUs graciously provided by Eric Hartford for the full-parameter fine-tuning of the model.

<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>

Safety

...